Reihaneh Rabbany

hussein.abdallah@mail.concordia.ca

Biographie

Reihaneh Rabbany est professeure adjointe à l'École d'informatique de l'Université McGill. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également membre du corps enseignant du Centre pour l’étude de la citoyenneté démocratique de McGill. Avant de se joindre à l’Université McGill, elle a été boursière postdoctorale à la School of Computer Science de l'Université Carnegie Mellon. Elle a obtenu un doctorat à l’Université de l’Alberta, au Département d'informatique. Elle dirige le laboratoire de données complexes, dont les recherches se situent à l'intersection de la science des réseaux, de l'exploration des données et de l'apprentissage automatique, et se concentrent sur l'analyse des données interconnectées du monde réel et sur les applications sociales.

Étudiants actuels

Hussein Abdallah

Postdoctorat - McGill

Oumaima Amezgar

Doctorat - UdeM

Superviseur⋅e principal⋅e :

oumaima.amezgar@mila.quebec

Aurélien Bück-Kaeffer

Maîtrise recherche - McGill

aurelien.buck-kaeffer@mila.quebec

Andrea Cini

Visiteur de recherche indépendant - Mila

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

mcorriveau303@gmail.com

Johnny Dellas

Doctorat - McGill

john.dellas@mila.quebec

Islam Eldifrawi

Visiteur de recherche indépendant - University of Sherbrooke

sulum821991@gmail.com

sophia.garrel@mila.quebec

Aarash Feizi

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice de recherche - McGill

Nicholas Gaudet

Doctorat - UdeM

Superviseur⋅e principal⋅e :

nicholas.gaudet@mila.quebec

mattia.greiche@mila.quebec

Mattia Greiche

Stagiaire de recherche - McGill

Nazia Hossain

Stagiaire de recherche - McGill University

nazia.hossain@mila.quebec

Shenyang Huang

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Anne Imouza

Doctorat - McGill

Superviseur⋅e principal⋅e :

anne.imouza@mila.quebec

Emma Kondrup

Doctorat - McGill

Co-superviseur⋅e :

Catherine Régis

emma.kondrup@mila.quebec

Andrew Lin

Stagiaire de recherche - McGill University

andrew.lin@mila.quebec

victor.livernoche@mila.quebec

Victor Livernoche

Doctorat - McGill

shahrad.mohammadzadeh@mila.quebec

Sitao Luan

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Doina Precup

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

nardin.nakhla@mila.quebec

Farimah Poursafaei

Collaborateur·rice alumni - McGill

farimah.poursafaei@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

meissa.rebbani@mila.quebec

sebastian.sabry@mila.quebec

Sebastian Sabry

Maîtrise recherche - McGill University

dorsaf.sallami@mila.quebec

Dorsaf Sallami

Visiteur de recherche indépendant - McGill

jacob.mila.handle@tianshome.com

Sneheel Sarangi

Maîtrise recherche - McGill

sneheelsarangi@gmail.com

Maîtrise recherche - McGill University

shiyan.wliu@gmail.com

Vidya Sujaya

Maîtrise recherche - McGill

vidya.sujaya@mila.quebec

Jacob-Junqi Tian

Maîtrise recherche - McGill

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

aaron.tjandra@mila.quebec

Li Wei Wang

Maîtrise recherche - McGill

liweiwang2002@gmail.com

Tong Wu

Baccalauréat - McGill University

Co-superviseur⋅e :

chin-chen.yang@mila.quebec

Doctorat - McGill

Brian Yang

Doctorat - McGill

brian.yang@mila.quebec

Maîtrise recherche - McGill

xueli.zhao@mila.quebec

Sveta Zhuk

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

svetlana.zhuk@mila.quebec

Billets de blogue

Un groupe hétéroclite de huit jeunes adultes se tient serré sur un toit, souriant et riant avec la silhouette de la ville en arrière-plan. Deux encarts circulaires mettent en évidence une caméra vintage tenue par l'un des membres du groupe, soulignant l'élément deepfake.

16 décembre 2025

Démasquer les deepfakes grâce à l'IA

par

Victor Livernoche

Reihaneh Rabbany

Lire l'article

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

3 août 2021

Flight-SEIR : incorporer les données de vol pour améliorer la modélisation épidémiologique et la prévention d’éclosions de maladies infectieuses

par

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks

Ta Thanh Thuy

Jiaqi Zhu

Xuan Liu

Lin Shang

Lihui Chen

Zheng Yilun

Sitao Luan

Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and conte… (voir plus)nt moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.

2025-12-31

Unpublished (publié)

AI Epistemic Risks: Emerging Mechanisms &amp; Evidence

Mick Yang

Stephen Casper

Jonathan Stray

Jasmine Li

Cameron Jones

Anna Gausen

Natasha Jacques

Brian Christian

Bálint Gyevnár

Hannah Rose Kirk

Zhonghao He

Dan Zhao (285025)

Siao Si Looi

J. Levy

Kobi Hackenburg

Elizabeth Seger

Matt Kowal

Michelle Malonza

Luke Hewitt

Hause Lin … (voir 10 de plus)

Maarten Sap

Dylan Hadfield-Menell

Thomas Costello

David Rand

Atoosa Kasirzadeh

Gordon Pennycook

Yoshua Bengio

Kellin Pelrine

2025-12-31

SSRN Electronic Journal (accepté)

Grounding Computer Use Agents on Human Demonstrations

Aarash Feizi

Shravan Nayak

Xiangru Jian

Kevin Qinghong Lin

Kaixin Li

Rabiul Awal

Xing Han Lu

Johan Obando-Ceron

Juan A. Rodriguez

Nicolas Chapados

David Vázquez

Adriana Romero-Soriano

Perouz Taslakian

Christopher Pal

Spandana Gella

Sai Rajeswar

Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen eleme… (voir plus)nts. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Position: Time to Close The Validation Gap in LLM Social Simulations

Maximilian Puelma Touzel

Sneheel Sarangi

Aurélien Bück-Kaeffer

Zachary Yang

LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy an… (voir plus)alysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse, inconsistent, and rarely shared across disciplinary silos. We argue this creates a serious risk: premature deployment of unvalidated simulators in high-stakes domains. Our position is that the field must pivot from expansion to consolidation, prioritizing methodological standardization—shared benchmarks, open data, and reproducible evaluation protocols grounded in social science and complex systems research. We outline a concrete research program organized around specific learning problems/benchmarks, providing a path toward answering the fundamental question: when are LLM social simulations useful modelling objects?

2025-12-31

International Conference on Machine Learning (Accept (regular))

Large Language Model Applications in the Algebra Domain: A Systematic Review

Yajie Song

Yimei Zhang

Doina Precup

Maria Cutumisu

2025-12-05

Technology, Knowledge and Learning (publié)

MiNT: Multi-Network Transfer Benchmark for Temporal Graph Learning

Kiarash Shamsi

Tran Gia Bao Ngo

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

Temporal Graph Learning (TGL) aims to discover patterns in evolving networks or temporal graphs and leverage these patterns to predict futur… (voir plus)e interactions. However, most existing research focuses on learning from a single network in isolation, leaving the challenges of within-domain and cross-domain generalization largely unaddressed. In this study, we introduce a new benchmark of 84 real-world temporal transaction networks and propose **Temporal Multi-network Transfer (MiNT)**, a pre-training framework designed to capture transferable temporal dynamics across diverse networks. We train MiNT models on up to 64 transaction networks and evaluate their generalization ability on 20 held-out, unseen networks. Our results show that MiNT consistently outperforms individually trained models, revealing a strong relation between the number of pre-training networks and transfer performance. These findings highlight scaling trends in temporal graph learning and underscore the importance of network diversity in improving generalization. This work establishes the first large-scale benchmark for studying transferability in TGL and lays the groundwork for developing Temporal Graph Foundation Models. Our code is available at https://github.com/benjaminnNgo/ScalingTGNs

2025-12-02

Neural Information Processing Systems (Accept (poster))

Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data

Soroush Omranpour

Transformers are now ubiquitous for sequence modeling tasks, but their extension to multi-dimensional data remains a challenge due to the qu… (voir plus)adratic cost of the attention mechanism. In this paper, we propose Higher-Order Transformers (HOT), a novel architecture designed to efficiently process data with more than two axes, i.e. higher-order tensors. To address the computational challenges associated with high-order tensor attention, we introduce a novel Kronecker factorized attention mechanism that reduces the attention cost to quadratic in each axis' dimension, rather than quadratic in the total size of the input tensor. To further enhance efficiency, HOT leverages kernelized attention, reducing the complexity to linear. This strategy maintains the model's expressiveness while enabling scalable attention computation. We validate the effectiveness of HOT on two high-dimensional tasks, including multivariate time series forecasting, and 3D medical image classification. Experimental results demonstrate that HOT achieves competitive performance while significantly improving computational efficiency, showcasing its potential for tackling a wide range of complex, multi-dimensional data.

2025-11-15

TMLR (accepté)

Reframing AI-for-Good: Radical Questioning in AI for Human Trafficking Interventions

Gabriel Lefebvre

This paper introduces Radical Questioning (RQ), a structured, pre-design ethics framework developed to assess whether artificial intelligenc… (voir plus)e (AI) should be applied to complex social problems rather than merely how. While much of responsible AI development focuses on aligning systems with principles such as fairness, transparency, and accountability, it often begins after the decision to build has already been made, implicitly treating the deployment of AI as a given rather than a question in itself. In domains such as human trafficking, marked by contested definitions, systemic injustice, and deep stakeholder asymmetries, such assumptions can obscure foundational ethical concerns. RQ offers an upstream, deliberative process for surfacing these concerns before design begins. Drawing from critical theory, participatory ethics, and relational responsibility, RQ formalizes a five-step framework to interrogate problem framings, confront techno-solutionist tendencies, and reflect on the moral legitimacy of intervention. Developed through interdisciplinary collaboration and engagement with survivor-led organizations, RQ was piloted in the domain of human trafficking (HT) which is a particularly high-stakes and ethically entangled application area. Its use led to a fundamental design shift: away from automated detection tools and toward survivor-controlled, empowerment-based technologies. We argue that RQ's novelty lies in both its temporal position, i.e, prior to technical design, and its orientation toward domains where harm is structural and ethical clarity cannot be achieved through one-size-fits-all solutions. RQ thus addresses a critical gap between abstract principles of responsible AI and the lived ethical demands of real-world deployment.

2025-10-14

AAAI/ACM Conference on AI, Ethics, and Society (publié)

TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs

Tran Gia Bao Ngo

Jure Leskovec

Michael M. Bronstein

Matthias Fey

Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like Py… (voir plus)Torch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over time, lacks comparable infrastructure. Existing TG libraries are often tailored to specific architectures, hindering support for diverse models in this rapidly evolving field. Additionally, the divide between continuous- and discrete-time dynamic graph methods (CTDG and DTDG) limits direct comparisons and idea transfer. To address these gaps, we introduce Temporal Graph Modelling (TGM), a research-oriented library for ML on temporal graphs, the first to unify CTDG and DTDG approaches. TGM offers first-class support for dynamic node features, time-granularity conversions, and native handling of link-, node-, and graph-level tasks. Empirically, TGM achieves an average 7.8x speedup across multiple models, datasets, and tasks compared to the widely used DyGLib, and an average 175x speedup on graph discretization relative to available implementations. Beyond efficiency, we show in our experiments how TGM unlocks entirely new research possibilities by enabling dynamic graph property prediction and time-driven training paradigms, opening the door to questions previously impractical to study. TGM is available at https://github.com/tgm-team/tgm

2025-10-07

ArXiv (prépublication)

$\texttt{BluePrint}$: A Social Media User Dataset for LLM Persona Evaluation and Training

Aur'elien Buck-Kaeffer

Je Qin Chooi

Dan Zhao

Maximilian Puelma Touzel

Kellin Pelrine

Zachary Yang

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethi… (voir plus)cally or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.

2025-09-26

ArXiv (prépublication)

Are Large Language Models Good Temporal Graph Learners?

Zifeng Ding

Michael M. Bronstein

Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. Wh… (voir plus)ile a broad range of literature has explored the graph-reasoning capabilities of LLMs, including their use of predictors on graphs, the application of LLMs to dynamic graphs -- real world evolving networks -- remains relatively unexplored. Recent work studies synthetic temporal graphs generated by random graph models, but applying LLMs to real-world temporal graphs remains an open question. To address this gap, we introduce Temporal Graph Talker (TGTalker), a novel temporal graph learning framework designed for LLMs. TGTalker utilizes the recency bias in temporal graphs to extract relevant structural information, converted to natural language for LLMs, while leveraging temporal neighbors as additional information for prediction. TGTalker demonstrates competitive link prediction capabilities compared to existing Temporal Graph Neural Network (TGNN) models. Across five real-world networks, TGTalker performs competitively with state-of-the-art temporal graph methods while consistently outperforming popular models such as TGN and HTGN. Furthermore, TGTalker generates textual explanations for each prediction, thus opening up exciting new directions in explainability and interpretability for temporal link prediction. The code is publicly available at https://github.com/shenyangHuang/TGTalker.

2025-09-21

NeurIPS.cc/2025/Workshop/NPGML (poster)

OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection

Akshatha Arodi

Ga'etan Marceau Caron

Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically… (voir plus) sensitive contexts. This challenge is amplified by the increasing realism of modern generative models, which our human perception study confirms are often indistinguishable from real images. Yet, existing deepfake detection benchmarks rely on outdated generators or narrowly scoped datasets (e.g., single-face imagery), limiting their utility for real-world detection. To address these gaps, we present OpenFake, a large politically grounded dataset specifically crafted for benchmarking against modern generative models with high realism, and designed to remain extensible through an innovative crowdsourced adversarial platform that continually integrates new hard examples. OpenFake comprises nearly four million total images: three million real images paired with descriptive captions and almost one million synthetic counterparts from state-of-the-art proprietary and open-source models. Detectors trained on OpenFake achieve near-perfect in-distribution performance, strong generalization to unseen generators, and high accuracy on a curated in-the-wild social media test set, significantly outperforming models trained on existing datasets. Overall, we demonstrate that with high-quality and continually updated benchmarks, automatic deepfake detection is both feasible and effective in real-world settings.

2025-09-10

ArXiv (prépublication)